common sense thinking. We might need to move into a strange more abstract world,
more readily analyzable in terms of mathematics.” And Kitano (2002a) emphasizes
that “computational biology, through pragmatic modeling and theoretical explora-
tion, provides a powerful foundation from which to address critical scientific ques-
tions head-on.”
The requirement to merge experimental techniques and theoretical concepts in
the investigation of biological objects has been acknowledged, for example, by Kitano
(2002a): “To understand complex biological systems requires the integration of ex-
perimental and computational research – in other words a systems biology approach.”
Levchenko (2003) recommends “the systems biology approach, relying on computa-
tional modeling coupled with various experimental techniques and methodologies,
… combining the dynamical view of rapidly evolving responses and the structural
view arising from high-throughput analyses of the interacting species.” Ideker and
colleagues (2001) state, “Systems biology studies biological systems by systematically
perturbing them (biologically, genetically, or chemically); monitoring the gene, pro-
tein, and informational pathway responses; integrating these data; and ultimately,
formulating mathematical models that describe the structure of the system and its
response to individual perturbations.”
Aebersold and colleagues (2000) see the fundamental experimental contribution
in large-scale facilities for genome-wide analyses, including DNA sequencing, gene
expression measurements, and proteomics, while Hood (2003) explains his path to
systems biology in the following way: “Our view and how we practice biology have
been profoundly changed by the Human Genome Project.”
Importantly, it has been discovered that cellular regulation is organized into com-
plex networks and that the various interactions of network elements in time and space
must be studied. Kitano (2002 b) stresses that “[t]o understand biology at the system
level, we must examine the structure and dynamics of cellular and organismal func-
tion, rather than the characteristics of isolated parts of a cell or organism. Properties
of systems, such as robustness, emerge as central issues, and understanding these
properties may have an impact on the future of medicine.” Kholodenko and collea-
gues want to “untangle the wires” and “trace the functional interactions in signaling
and gene networks.” Levchenko (2003) sees advantages in understanding signaling:
“A new view of signaling networks as systems consisting of multiple complex ele-
ments interacting in a multifarious fashion is emerging, a view that conflicts with the
single-gene or protein-centric approach common in biological research. The postge-
nomic era has brought about a different, network-centric methodology of analysis,
suddenly forcing researchers toward the opposite extreme of complexity, where the
networks being explored are, to a certain extent, intractable and uninterpretable.”
There are many fields of application besides the understanding of cellular regula-
tion. With respect to modeling of the heart as whole organ, Noble (2002) discusses
that “[s]uccessful physiological analysis requires an understanding of the functional
interactions between the key components of cells, organs, and systems, as well as
how these interactions change in disease states. This information resides neither in
the genome nor even in the individual proteins that genes code for. It lies at the level
of protein interactions within the context of subcellular, cellular, tissue, organ, and
VI
Preface